The 12 Best Data Mining Books

7 min read

Data mining is an increasingly important skill for businesses to stay informed and up-to-date with the latest trends. In order to make sure one has a full understanding of this field, it’s essential to read from experts in data mining who have put together comprehensive books on the subject matter. This article provides reviews and recommendations for some of the best data mining books available today, helping readers explore topics like predictive modeling, machine learning algorithms, concepts and techniques that are critical for modern business analytics.

  1. Data Science for Business

    Data Science for Business
    Foster Provost, Tom Fawcett
    Published in 2013

    Data Science for Business by Foster Provost and Tom Fawcett is an outstanding book that guides readers in understanding how to use data-analytical thinking to extract knowledge from the copious amounts of data available today. This publication provides a thorough explanation of various data mining techniques, along with real-world business examples, to illustrate these principles. The authors’ extensive experience in applied research allows them to offer insight into how companies can make use of their data as a valuable asset through intelligent investment strategies. Additionally, this text offers guidance on interviewing potential candidates for Data Science roles as well as instructions on applying these methods when making decisions within organizations. Overall, Data Science for Business is essential reading material for all those aiming to utilize big data opportunities and achieve success via informed decision-making processes.

  2. The Elements of Statistical Learning

    The Elements of Statistical Learning
    Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Published in 2016

    The Elements of Statistical Learning is an invaluable resource for anyone interested in data mining, from statisticians to scientists and industry professionals. This second edition provides a comprehensive treatment of the important topics covering supervised and unsupervised learning such as neural networks, support vector machines, classification trees, boosting methods and more. There are four new chapters focusing on graphical models, random forests, ensemble methods and least angle regression & path algorithms for lassoing non-negative matrix factorization along with spectral clustering making this book worth obtaining. It also offers material suitable for several courses due to its well-written content that gives a good overview of statistical learning combined with mathematical issues relevant to practice. With correct derivations of popular machine learning algorithm methods derived after careful consideration of statistical frameworks it stands out by actually doing something (huge) with math which makes it essential both theoretically inclined readers looking for entry points into the area or practitioners seeking additional tools in their toolbox alike.

  3. Practical Statistics for Data Scientists

    Practical Statistics for Data Scientists
    Peter Bruce, Andrew Bruce, Peter Gedeck
    Published in 2020

    Practical Statistics for Data Scientists is an excellent book published by O’Reilly which provides a comprehensive look at statistical methods and their application in data science. This edition has been updated with Python examples, giving readers a more modern perspective regarding how to use statistics when dealing with big data sets. Written by Peter Bruce, Andrew Bruce, and Peter Gedeck, this useful guide offers advice on avoiding misuse of such techniques as well as discussing key classification tools used to predict categorization of records. The authors also discuss regression analysis that can be used to estimate outcomes or detect anomalies while providing information on machine learning & unsupervised learning algorithms employed in extracting meaning from unlabeled data. Readers familiar with the R or Python programming languages will find this quick reference bridges the gap between basic stats and understanding its implementation within the world of data science; making it ideal for aspiring scientists looking to make sense out of vast amounts of complex digital information.

  4. Data Mining for Business Analytics

    Data Mining for Business Analytics
    Published in 2016

    Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an exceptionally comprehensive resource for data scientists, analysts, researchers and practitioners. Featuring hands-on examples with the statistical package from SAS Institute – JMP Pro®, this book provides readers a theoretical as well as practical understanding of the key techniques in data mining such as predictive models used in classification or prediction. With detailed summaries at the start of each chapter to provide an outline of topics discussed, Data Mining includes chapters on various applications like dimension reduction methods and clustering along with classical linear/logistic regression & trees analysis too. Furthermore, real-world case studies are provided to demonstrate these concepts while end-of-chapter exercises allow readers to expand their comprehension further still. Additionally there’s a companion website offering datasets & solutions plus slides for instructors – making it ideal for advanced undergraduates & graduate students looking into courses about analytics or business intelligence.

  5. Data Mining: Concepts and Techniques

    Data Mining: Concepts and Techniques
    Jiawei Han, Jian Pei, Hanghang Tong
    Published in 2022

    This book, Data Mining: Concepts and Techniques (Fourth Edition), offers an in-depth exploration into the realm of knowledge discovery from data. It provides a comprehensive breakdown of the steps necessary to preprocess, characterize, warehouse and partition various kinds of data stored in large databases. The text outlines methods for attaining frequent patterns, associations and correlations; classifying data; constructing models; clustering analysis; detecting outliers as well as introducing state-of-the art deep learning concepts. Furthermore this book covers current trends, applications and further research possibilities within the field of data mining. As such it serves as an excellent reference tool for students studying information systems or computer science related disciplines with its vendor neutral approach that allows readers to implement ideas using their preferred methodologies accompanied by many worked examples throughout.

  6. Data Mining: Practical Machine Learning Tools and Techniques

    Data Mining: Practical Machine Learning Tools and Techniques
    Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
    Published in 2016

    Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, is an authoritative guide to data mining for readers of all levels. It provides detailed instructions on preparing inputs, interpreting outputs, evaluating results and mastering the algorithmic methods used in successful data mining approaches. This edition has been updated with new chapters on probabilistic methods and deep learning as well as a downloadable version of the popular WEKA software from University of Waikato. The book offers practical advice along with plenty of tips and techniques for performance improvement through transforming input or output machine learning methods. With comprehensive online courses introducing applications found within its pages, this work serves both novice learners who are just getting started as well as advanced students looking to expand their knowledge base.

  7. R for Everyone: Advanced Analytics and Graphics

    R for Everyone: Advanced Analytics and Graphics
    Jared Lander
    Published in 2017

    R for Everyone: Advanced Analytics and Graphics, written by professional data scientist Jared P. Lander is the perfect tutorial for anyone wanting to master statistical programming or modeling. This comprehensive guide focuses on the essential 20 percent of R functionality that can be used to accomplish 80 percent of modern data tasks. It starts with absolute basics, offering extensive hands-on practice and sample code before walking through constructing several complete models – linear as well as nonlinear – plus some useful data mining techniques. The reader will learn how to install and use R; understand variable types, vectors and functions; manipulate strings using regular expressions; create probability distributions; carry out basic statistics calculations such a mean, standard deviation and t-tests; employ group manipulations for improved efficiency in programs; combine & reshape multiple datasets within an intuitive framework. Additionally there are chapters devoted to preventing overfitting via Elastic Net & Bayesian methods, analyzing time series data & clustering algorithms as well as preparing reports with knitr/devtools/Rcppplus more! An indispensable resource for beginners through advanced users looking to get up& running quickly mastering this powerful language!

  8. Neural Networks and Deep Learning: A Textbook

    Neural Networks and Deep Learning: A Textbook
    Charu C. Aggarwal
    Published in 2018

    Neural Networks and Deep Learning: A Textbook is an excellent resource for graduate students, researchers, and practitioners of deep learning. Written by a renowned author in the field, this comprehensive book covers both classical and modern models as well as their applications within many different areas such as recommender systems, machine translation, image captioning, reinforcement-learning based gaming and text analytics. In order to provide a better understanding of how neural architectures are designed for various problems it also discusses traditional machine learning methods like support vector machines or linear/logistic regression which can be seen as special cases of neural networks. Furthermore with its detailed explanation on training & regularization plus advanced topics including recurrent neural networks & convolutional neural networks makes this textbook indispensable when working with deep learning algorithms. Through plenty exercises accompanied by solution manuals teachers have access to valuable material while readers get even more insight into useful techniques due to application-centric views provided throughout the chapters. All in all Neural Networks and Deep Learning: A Textbook provides an extremely thorough coverage that should not be missed out on!

  9. Everybody Lies

    Everybody Lies
    Seth Stephens-Davidowitz
    Published in 2018

    Everybody Lies by Seth Stephens-Davidowitz is an exploration of the vast amounts of data available in today’s digital world and what it reveals about us. The book provides a humorous, sometimes shocking look at human behavior, offering surprising insights into topics like race, gender, economics and more drawn from big data. It challenges readers to think differently about how we view ourselves and our world. With fascinating studies and experiments on how we really live and act, this book dives deep into the power behind digital truth serums—revealing biases deeply embedded within us that can help shape culture for the better. Filled with intriguing anecdotes and counterintuitive facts from leading researchers in the industry, Everybody Lies will open your eyes to a new age of understanding humanity through insightful analysis backed up by hard evidence.

  10. Fundamentals of Data Engineering: Plan and Build Robust Data Systems

    Fundamentals of Data Engineering: Plan and Build Robust Data Systems
    Joe Reis, Matt Housley
    Published in 2022

    Fundamentals of Data Engineering: Plan and Build Robust Data Systems is an essential resource for any data engineer looking to get up-to-speed on the latest in cloud technologies, architectures and processes. Joe Reis and Matt Housley provide a concise overview of the entire data engineering landscape that cuts through marketing hype when choosing the best options. This book covers everything from statistical modeling, forecasting, machine learning to security across the lifecycle with practical applications designed to serve downstream customers. It’s easy to read yet packed full of information making it perfect for both seasoned professionals as well as those just starting out in this field. If you’re serious about understanding how modern technology works then look no further than Fundamentals of Data Engineering – an absolute must have!

  11. Trustworthy Online Controlled Experiments

    Trustworthy Online Controlled Experiments
    Ron Kohavi
    Published in 2020

    Trustworthy Online Controlled Experiments is a comprehensive and authoritative guide to A/B testing. Authored by leading experimentation experts from Microsoft, Google, LinkedIn and other tech giants, this book provides an invaluable resource for executives, leaders, researchers or engineers eager to optimize their product features using online controlled experiments. It offers practical advice on how to use the scientific method to evaluate hypotheses; define key metrics and Overall Evaluation Criterion; test results trustworthiness; build a scalable platform that lowers cost of experiments close to zero; understand statistical issues in practice as well as pitfalls like carryover effects and Twyman’s law. This bible of digital-age decision making covers all aspects needed for success with trustworthy online controlled experiments – enabling readers to make data-driven decisions efficiently while avoiding costly mistakes.

  12. Practical SQL, 2nd Edition

    Practical SQL, 2nd Edition
    Anthony DeBarros
    Published in 2022

    Practical SQL, 2nd Edition is an approachable and comprehensive guide to Structured Query Language (SQL) for beginners and experienced database administrators alike. Written by data analyst Anthony DeBarros, the book focuses on using SQL to uncover stories within data. Examples are drawn from real-world datasets such as US Census demographics, New York City taxi rides and earthquakes from US Geological Survey.. Readers learn how to create databases with their own data, filter and sort information to identify patterns, use functions for basic math or more advanced statistical operations, analyze spatial data and automate tasks. The updated second edition of Practical SQL contains two new chapters covering setup instructions and PostgreSQL’s compatibility with JSON format in addition to revised sections on the latest features of the language. It presents concepts clearly through intriguing exercises that make learning easy without sacrificing depth of knowledge – a must have tool for anyone looking to build powerful databases effectively!

Additional resources

If you are looking for more information on Data Mining, here are some helpful blogs and websites:

  • Data Science Central is an online resource for data scientists, providing a variety of articles, tutorials, and resources.
  • Kaggle is a platform for predictive modeling and analytics competitions.
  • DataCamp offers interactive tutorials and courses on data science and analytics.
  • The Data Mining Blog by Philippe Fournier-Viger contains interesting articles and discussions on system architectures for data mining applications.

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6 Replies to “The 12 Best Data Mining Books”

  1. I don’t think the title of your article matches the content lol. Just kidding, mainly because I had some doubts after reading the article.

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